"Parametric Bootstrap Differentially-Private Confidence Intervals"

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Scripts:

- "DPCIs-expfam.py": main Python functions and driver to run confidence interval experiments
- "DPCIs-expfam-mv.py": multivariate version of Gaussian of known variance (analysis on dim 0)
- "DPCIs-OLS.py": main Python functions and driver to run confidence interval experiments for OLS
- "functions.py": helper functions, including sensitivity and privatization mechanisms

Requirements:

NumPy, SciPy, argparse, matplotlib.pyplot, pandas, seaborn

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To reproduce the experiments (example for DPCIs for exponential families):

1) choose experiment parameters:

	--N	[int] data size
	
	--theta	[float] first parameter (manually set to parameter vector if using multivariate version 'CImv.py')
	
	--theta2 [float] second parameter, if any (manually set to parameter vector if using multivariate version 'CImv.py')
	
	--d	[str] distribution:
			'poisson' -> Poisson 
			'gaussian' -> Gaussian of known variance
			'gaussian2' -> Gaussian of unknown variance
			'gamma' -> Gamma of known shape
			'gaussianMV' -> multivariate Gaussian of known variance (use 'CImv.py')
	
	--mode	[str] 
		'empirical' for bootstrap quantile CIs
		'analytic' for standard normal CIs
	
	--e	[float] differential privacy parameter epsilon (for example 0.1 or 0.5)
	
	--clip	[bool]
		'True' -> clamp data to the sensitivity bounds found on a n=1000 dataset
		'False' -> conservative sensitivity, no clamping to the sensitivity bounds
	
	--rng	[float]
		data range radius for gaussian experiment comparison
		if rng=0., range is computed out of a surrogate dataset of size 1000
	
	--rho	[float]
		for two-parameter privatization (like Gaussian with unknown variance)
		share of privacy budget to allocate to privatization of the first parameter
		(1-rho will be allocated to privatize the other parameter)


2) run driver with choice of experiment parameters

3) results will be saved in .npy